Photoplethysmographic Prediction of the Ankle-Brachial Pressure Index through a Machine Learning Approach
Cardiovascular disease is a leading cause of death. Several markers have been proposed to predict cardiovascular morbidity. The ankle-brachial index (ABI) marker is defined as the ratio between the ankle and the arm systolic blood pressures, and it is generally assessed through sphygmomanometers. An...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-03-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/10/6/2137 |
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| author | David Perpetuini Antonio Maria Chiarelli Daniela Cardone Sergio Rinella Simona Massimino Francesco Bianco Valentina Bucciarelli Vincenzo Vinciguerra Giorgio Fallica Vincenzo Perciavalle Sabina Gallina Arcangelo Merla |
| author_facet | David Perpetuini Antonio Maria Chiarelli Daniela Cardone Sergio Rinella Simona Massimino Francesco Bianco Valentina Bucciarelli Vincenzo Vinciguerra Giorgio Fallica Vincenzo Perciavalle Sabina Gallina Arcangelo Merla |
| author_sort | David Perpetuini |
| collection | DOAJ |
| description | Cardiovascular disease is a leading cause of death. Several markers have been proposed to predict cardiovascular morbidity. The ankle-brachial index (ABI) marker is defined as the ratio between the ankle and the arm systolic blood pressures, and it is generally assessed through sphygmomanometers. An alternative tool for cardiovascular status assessment is Photoplethysmography (PPG). PPG is a non-invasive optical technique that measures volumetric blood changes induced by pulse pressure propagation within arteries. However, PPG does not provide absolute pressure estimation, making assessment of cardiovascular status less direct. The capability of a multivariate data-driven approach to predict ABI from peculiar PPG features was investigated here. ABI was measured using a commercial instrument (Enverdis Vascular Explorer, VE-ABI), and it was then used for a General Linear Model estimation of ABI from multi-site PPG in a supervised learning framework (PPG-ABI). A Receiver Operating Characteristic (ROC) analysis allowed to investigate the capability of PPG-ABI to discriminate cardiovascular impairment as defined by VE-ABI. Findings suggested that ABI can be estimated form PPG (r = 0.79) and can identify pathological cardiovascular status (AUC = 0.85). The advantages of PPG are simplicity, speed and operator-independency, allowing extensive screening of cardiovascular status and associated cardiovascular risks. |
| first_indexed | 2024-12-13T06:18:53Z |
| format | Article |
| id | doaj.art-d2f6f7bb5f5b474794f0d4cba9c0964a |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-12-13T06:18:53Z |
| publishDate | 2020-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-d2f6f7bb5f5b474794f0d4cba9c0964a2022-12-21T23:56:54ZengMDPI AGApplied Sciences2076-34172020-03-01106213710.3390/app10062137app10062137Photoplethysmographic Prediction of the Ankle-Brachial Pressure Index through a Machine Learning ApproachDavid Perpetuini0Antonio Maria Chiarelli1Daniela Cardone2Sergio Rinella3Simona Massimino4Francesco Bianco5Valentina Bucciarelli6Vincenzo Vinciguerra7Giorgio Fallica8Vincenzo Perciavalle9Sabina Gallina10Arcangelo Merla11Institute for Advanced Biomedical Technologies, Department of Neuroscience and Imaging, University G. D’Annunzio of Chieti-Pescara, Via Luigi Polacchi 13, 66100 Chieti, ItalyInstitute for Advanced Biomedical Technologies, Department of Neuroscience and Imaging, University G. D’Annunzio of Chieti-Pescara, Via Luigi Polacchi 13, 66100 Chieti, ItalyInstitute for Advanced Biomedical Technologies, Department of Neuroscience and Imaging, University G. D’Annunzio of Chieti-Pescara, Via Luigi Polacchi 13, 66100 Chieti, ItalyPhysiology Section, Department of Biomedical and Biotechnological Sciences, University of Catania, Via Santa Sofia 97, 95123 Catania, ItalyPhysiology Section, Department of Biomedical and Biotechnological Sciences, University of Catania, Via Santa Sofia 97, 95123 Catania, ItalyInstitute of Cardiology, University G. D’Annunzio of Chieti-Pescara, Via Dei Vestini 5, 66100 Chieti, ItalyInstitute of Cardiology, University G. D’Annunzio of Chieti-Pescara, Via Dei Vestini 5, 66100 Chieti, ItalySTMicroelectronics, ADG R&D, Stradale Primosole 50, 95121 Catania, ItalySTMicroelectronics, ADG R&D, Stradale Primosole 50, 95121 Catania, ItalyPhysiology Section, Department of Biomedical and Biotechnological Sciences, University of Catania, Via Santa Sofia 97, 95123 Catania, ItalyInstitute for Advanced Biomedical Technologies, Department of Neuroscience and Imaging, University G. D’Annunzio of Chieti-Pescara, Via Luigi Polacchi 13, 66100 Chieti, ItalyInstitute for Advanced Biomedical Technologies, Department of Neuroscience and Imaging, University G. D’Annunzio of Chieti-Pescara, Via Luigi Polacchi 13, 66100 Chieti, ItalyCardiovascular disease is a leading cause of death. Several markers have been proposed to predict cardiovascular morbidity. The ankle-brachial index (ABI) marker is defined as the ratio between the ankle and the arm systolic blood pressures, and it is generally assessed through sphygmomanometers. An alternative tool for cardiovascular status assessment is Photoplethysmography (PPG). PPG is a non-invasive optical technique that measures volumetric blood changes induced by pulse pressure propagation within arteries. However, PPG does not provide absolute pressure estimation, making assessment of cardiovascular status less direct. The capability of a multivariate data-driven approach to predict ABI from peculiar PPG features was investigated here. ABI was measured using a commercial instrument (Enverdis Vascular Explorer, VE-ABI), and it was then used for a General Linear Model estimation of ABI from multi-site PPG in a supervised learning framework (PPG-ABI). A Receiver Operating Characteristic (ROC) analysis allowed to investigate the capability of PPG-ABI to discriminate cardiovascular impairment as defined by VE-ABI. Findings suggested that ABI can be estimated form PPG (r = 0.79) and can identify pathological cardiovascular status (AUC = 0.85). The advantages of PPG are simplicity, speed and operator-independency, allowing extensive screening of cardiovascular status and associated cardiovascular risks.https://www.mdpi.com/2076-3417/10/6/2137photoplethysmography (ppg)ankle-brachial index (abi)arterial stiffnessmachine learningcardiovascular risk |
| spellingShingle | David Perpetuini Antonio Maria Chiarelli Daniela Cardone Sergio Rinella Simona Massimino Francesco Bianco Valentina Bucciarelli Vincenzo Vinciguerra Giorgio Fallica Vincenzo Perciavalle Sabina Gallina Arcangelo Merla Photoplethysmographic Prediction of the Ankle-Brachial Pressure Index through a Machine Learning Approach Applied Sciences photoplethysmography (ppg) ankle-brachial index (abi) arterial stiffness machine learning cardiovascular risk |
| title | Photoplethysmographic Prediction of the Ankle-Brachial Pressure Index through a Machine Learning Approach |
| title_full | Photoplethysmographic Prediction of the Ankle-Brachial Pressure Index through a Machine Learning Approach |
| title_fullStr | Photoplethysmographic Prediction of the Ankle-Brachial Pressure Index through a Machine Learning Approach |
| title_full_unstemmed | Photoplethysmographic Prediction of the Ankle-Brachial Pressure Index through a Machine Learning Approach |
| title_short | Photoplethysmographic Prediction of the Ankle-Brachial Pressure Index through a Machine Learning Approach |
| title_sort | photoplethysmographic prediction of the ankle brachial pressure index through a machine learning approach |
| topic | photoplethysmography (ppg) ankle-brachial index (abi) arterial stiffness machine learning cardiovascular risk |
| url | https://www.mdpi.com/2076-3417/10/6/2137 |
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